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Using Emulation to Engineer and Understand Simulations of Biological Systems

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JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
DateAccepted/In press - 1 Apr 2018
DatePublished (current) - 7 Jun 2018
Number of pages15
Original languageEnglish

Abstract

Modeling and simulation techniques have demonstrated success in studying biological systems. As the drive to better capture biological complexity leads to more sophisticated simulators, it becomes challenging to perform statistical analyses that help translate predictions into increased understanding. These analyses may require repeated executions and extensive sampling of high-dimensional parameter spaces: analyses that may become intractable due to time and resource limitations. Significant reduction in these requirements can be obtained using surrogate models, or emulators, that can rapidly and accurately predict the output of an existing simulator. We apply emulation to evaluate and enrich understanding of a previously published agent-based simulator of lymphoid tissue organogenesis, showing an ensemble of machine learning techniques can reproduce results obtained using a suite of statistical analyses within seconds. This performance improvement permits incorporation of previously intractable analyses, including multi-objective optimization to obtain parameter sets that yield a desired response, and Approximate Bayesian Computation to assess parametric uncertainty. To facilitate exploitation of emulation in simulation-focused studies, we extend our open source statistical package, spartan, to provide a suite of tools for emulator development, validation, and application. Overcoming resource limitations permits enriched evaluation and refinement, easing translation of simulator insights into increased biological understanding.

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© Copyright 2018

    Research areas

  • Analytical models, Approximate Bayesian Computation, Biological system modeling, Biological systems, Computational modeling, Emulation, Ensemble, Machine Learning, Machine learning, Mechanistic Modeling, Multi-Objective Optimization, Sensitivity Analysis, Uncertainty

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